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E

NHANCING

B

RAND

E

QUITY

T

HROUGH

F

LOW AND

T

ELEPRESENCE

:

A

C

OMPARISON OF

2D

AND

3D

V

IRTUAL

W

ORLDS

Fiona Fui-Hoon Nah

College of Business Administration, University of Nebraska–Lincoln, Lincoln, NE 68588 U.S.A. {fnah@unlnotes.unl.edu}

Brenda Eschenbrenner

College of Business and Technology, University of Nebraska at Kearney, Kearney, NE 68849 U.S.A. {eschenbrenbl@unk.edu}

David DeWester

College of Arts and Sciences, University of Nebraska–Lincoln, Lincoln, NE 68588 U.S.A. {ddewester2@unl.edu}

Appendix A

Summary of Literature on Flow and Telepresence in Online Environments

Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Steuer (1992) Vividness, Interactivity

Telepresence Virtual Reality Conceptual

Trevino and Webster (1992)

Tech. Type, Tech. Characteristic (Ease of Use), Ind. Diff. (Computer Skill) Flow – Control, Attention Focus, Curiosity, Intrinsic Interest Attitude, Effectiveness, Quantity, Barrier Reduction Computer-mediated Communication (E-mail, Voice Mail)

Survey Webster et al. (1993) Software Characteristics (Flexibility, Modifiability) Flow – Control, Attention Focus, Cognitive Enjoyment (Curiosity and Intrinsic Interest) Exploratory Behavior (Experimentation), System Use, Perceived Comm. Quantity, Perceived Software Usage in Work Setting Survey

(2)

Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Hoffman and Novak (1996) Control Char. (Skills, Challenges), Content Char. (Interactivity, Vividness), Process Char. (Goal-Directed, Experiential), Involvement, Focused Attention, Telepresence

Flow Consumer Learning,

Perceived Behavioral Control, Exploratory Behavior, Positive Subjective Experience Hypermedia Computer-mediated Environment Conceptual Lombard and Ditton (1997) Media Form (Vividness or Sensory Richness), Media Content (e.g., Task or Activity), Media User Variables Presence (or Telepresence) Arousal, Enjoyment, Involvement, Task Performance, Skills Training, Desensitization, Persuasion, Memory, Social Judgment, Parasocial Interaction/ Relationships

Virtual Environment Conceptual

Chen et al. (1999) Clear Goals, Immediate Feedback, Matched Skills and Challenges Flow – Merger of Action and Awareness, Concentration, Sense of Control Self-consciousness, Time Distortion, Autotelic Experience

Web Navigation Survey

Nel et al. (1999)

Web Site Type (Content, Audience Focus) Flow – Control, Attention Focus, Curiosity, Intrinsic Interest

Website Revisit Web Sites Experiment

Agarwal and Karahanna (2000) Personal Innovativeness, Playfulness Cognitive Absorption/Flow – Curiosity, Control, Temporal Dissociation, Focused Immersion, Heightened Enjoyment Perceived Ease of Use, Perceived Usefulness, Behavioral Intention IT (World Wide Web) Usage Survey Chen et al. (2000) Flow – Merger of Action and Awareness, Concentration, Loss of Self Consciousness, Time Distortion, Sense of Control, Telepresence, Enjoyment, Perceived Challenges

(3)

Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Novak et al. (2000) Skill/Control, Interactive Speed, Importance, Challenge/Arousal, Focused Attention, Telepresence/Time Distortion

Flow Online Shopping Survey

Rettie (2001) Goals, Feedback, Skills, Challenge Flow – Merging of Action and Awareness, Focused Concentration, Sense of Control, Loss of Self Consciousness, Time Distortion, Autotelic Experience

Internet Use Focused groups Koufaris (2002) Product Involvement, Web Skills, Value-Added Search Mechanisms, Challenges Flow – Shopping Enjoyment, Concentration

Intention to Return Online Shopping Survey

Luna et al. (2002) Balance of Challenges/Skills, Perceived Control, Unambiguous Demands, Focused Attention, Attitude toward Site

Flow Revisit Intent,

Purchase Intent

Purchase Online Shopping Experiment

Huang (2003) Complexity, Novelty, Interactivity Flow – Control, Attention, Curiosity, Interest Utilitarian Performance, Hedonic Performance

Web Sites Survey

Klein (2003) Media Richness, User Control

Telepresence Persuasion (Belief Strength, Attitude Intensity) Computer–mediated Environment Experiment Korzaan (2003) Flow Exploratory Behavior, Attitude Intention to Purchase

Online Shopping Survey Luna et al.

(2003)

Attention, Challenge, Interactivity, Attitude toward Site

Flow Purchase Intent,

Revisit Intent

Online Shopping Survey

Novak et al. (2003) Goal-directed vs. Experiential Activities, Skill, Challenge, Novelty, Importance

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Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Hsu and Lu (2004) Perceived Ease of Use

Flow Intention Online Games Survey

Jiang and Benbasat (2004) Visual Control, Functional Control Flow – Control, Attention Focus, Cognitive Enjoyment E-commerce Websites Experiment

Pace (2004) Goals and Navigation Behavior, Challenge and Skills, Attention Flow – Joy of Discovery, Reduced Awareness of Irrelevant Factors, Distorted Sense of Time, Merging of Action and Awareness, Sense of Control, Mental Alertness, Telepresence

Web Browsing Grounded Theory (Theoretical Sampling, Semi-Structured Interviews) Pilke (2004) Immediate Feedback, Clear Rules/Goals, Complexity, Dynamic Challenges

Flow World Wide Web Interviews

Reid (2004) Cognitive Ability, Volitional Control, Self-efficacy Flow and Playfulness Competence, Creativity, User Satisfaction

Virtual Reality Interviews, Experiment, Observation Skadberg and Kimmel (2004) Speed, Ease of Use, Attractiveness, Interactivity, Domain Knowledge/Skill, Information in the Web Site/Challenge Flow – Enjoyment, Time Distortion, Telepresence

Increased Learning Change of Attitude and Behavior

Web Browsing Survey

Kim et al. (2005)

Skills, Challenges, Focused Attention

Flow Online Games Survey

Saade and Bahli (2005) Cognitive Absorption/Flow – Temporal Dissociation, Focused Immersion, Heightened Enjoyment Perceived Ease of Use, Perceived Usefulness

Intention to Use Internet Learning Survey

Siekpe (2005) Flow – Challenges, Concentration, Curiosity, Control Intention to Purchase, Intention to Return

Online Shopping Survey

Suh and Lee (2005)

(Virtually High versus Low) Experiential Products

Telepresence Product Knowledge, Attitude, Purchase Intentions

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Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method

Chen (2006) Clear Goal, Potential Control, Immediate Feedback, Merger of Action and Awareness Flow – Telepresence, Time Distortion, Concentration, Loss of Self-consciousness Positivity of Affects, Enjoyable Feeling

Web Browsing Survey (Digitalized Experience Sampling Method) Li and Browne (2006)

Need for Cognition, Mood Flow – Focused Attention, Control, Curiosity, Temporal Dissociation

Online Experience Survey

Shin (2006) Skill, Challenge, Individual Differences Flow – Enjoyment, Telepresence, Focused Attention, Engagement, Time Distortion Achievement, Satisfaction Virtual Learning Environment Survey Tung et al. (2006) Product Involvement Flow – Control, Attention, Curiosity, Interest

Mood, Attitude Web Site

Advertising Experiment Choi et al. (2007) Learning Interface, Interaction, Instructor Attitude, Content

Flow Attitude Toward

E-learning, Learning Outcomes (Tech. Self-efficacy) E-learning Survey Chang and Wang (2008) Interactivity, Perceived Ease of Use Flow Perceived Usefulness, Attitude toward Use, Behavioral Intention Computer-mediated Communication Survey Chen et al. (2008) Flow – Control, Attention Focus, Curiosity and Interest Communication Outcome – Effectiveness, Quality, Volume Computer-mediated Communication Experiment Park et al. (2008) Control Char. (Skills, Challenges), Content Char. (Interactivity, Vividness), Process Char. (Extrinsic/Intrinsic Motivation)

Flow Brand Equity Virtual Worlds Conceptual

Guo and Poole (2009) Website Complexity, Clear Goal, Immediate Feedback, Congruence of Challenge and Skill

Flow – Concen-tration, Control, Mergence of Action and Awareness, Transformation of Time, Transcendence of Self, Autotelic Experience

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Reference/ Authors Antecedents Online Experience Direct Consequences Indirect Consequences Research Setting Research Method Hoffman and Novak (2009) Skill, Challenge, Interactivity, Telepresence, Attractiveness, Novelty, Playfulness, Personal Innovativeness, Content/Interface, Ease of Use, Positive Subjective Experience/Attitude Flow Learning, Control/Perceived Behavioral Control, Exploratory Behavior/Curiosity/ Discovery, Positive Subjective Experience/Attitude, Ease of Use, Perceived Usefulness, Purchase/ Behavioral Intention, Addictive Behavior

Purchase/Use Internet Conceptual

Shin (2009) Perceived Synchronicity

Flow Intention Virtual Worlds Survey

Ho and Kuo (2010)

Computer Attitudes Flow – Control, Focused Attention, Intrinsic Interest, Curiosity Learning Outcomes – Adaptation, Replication, Innovation E-learning Survey Lee and Chen (2010) Flow – Concentration, Enjoyment, Time Distortion, Telepresence Attitude, Controllability, Self-efficacy, Perceived Ease of Use Perceived Behavioral Control, Intention, Behavior, Perceived Usefulness

Online Shopping Survey

Nah et al (2010)

Balance of Skills and Challenges

Flow Brand Equity Behavioral

Intention

3D Virtual World Survey Zaman et al (2010) Telepresence, Perceived Control Flow – Enjoyment, Concentration Positive Affect, Exploratory Behavior Perceived Expected Creativity

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Appendix B

Items for Measures

Item

Construct and Measurement Items

Telepresence

(7-point Likert scale)

TP1

I forgot about my immediate surroundings when I was navigating the <hospital brand name> virtual tour.

TP2

When the virtual tour ended, I felt like I came back to the “real world” after a journey.

TP3

During the virtual tour, I forgot that I was in the middle of an experiment.

TP4

The computer-generated world seemed to be “somewhere I visited” rather than “something I saw.”

Enjoyment

(7-point Likert scale)

ENJ1

I found my virtual tour of <hospital brand name> enjoyable.

ENJ2

I found my virtual tour of <hospital brand name> boring. (Reverse coded)

ENJ3

I found my virtual tour of <hospital brand name> interesting.

ENJ4

I found my virtual tour of <hospital brand name> fun.

Brand Equity

(7-point Likert scale)

BE1

Even if another hospital offers the same quality of services as <hospital brand name>, I would prefer to use

the services of <hospital brand name>.

BE2

If there is another hospital as good as <hospital brand name>, I prefer to go to <hospital brand name>.

BE3

It makes sense to use the services of <hospital brand name> instead of services of any other hospitals even

if they are the same.

Behavioral Intention (i.e., intention to visit hospital)

(7-point Likert scale)

The header for the three intention items read: “Assuming that <hospital brand name> is available in your

area…”

INT1

…I would consider <hospital brand name> the next time I need a hospital service.

INT2

…I would recommend <hospital brand name> if a friend calls me to get my advice in his/her search for a

hospital.

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Appendix C

Descriptive Statistics, Reliability, Validity, and Common Method Variance

Descriptive Statistics

Table C1. Descriptive Statistics of Indicators

Items

Aggregate Data

2D Condition

3D Condition

Mean

Standard

Deviation

Mean

Standard

Deviation

Mean

Standard

Deviation

INT1

4.45

1.65

4.30

1.66

4.56

1.64

INT2

4.42

1.61

4.22

1.65

4.58

1.57

INT3

4.03

1.69

3.73

1.70

4.26

1.66

BE1

4.30

1.35

4.32

1.29

4.30

1.39

BE2

4.24

1.31

4.13

1.21

4.31

1.37

BE3

4.46

1.38

4.39

1.32

4.50

1.42

ENJ1

4.78

1.30

4.36

1.41

5.05

1.14

ENJ2

4.29

1.59

3.68

1.50

4.69

1.53

ENJ3

5.00

1.30

4.66

1.47

5.21

1.13

ENJ4

4.52

1.39

4.02

1.44

4.84

1.25

TP1

3.48

1.67

3.01

1.58

3.79

1.65

TP2

3.37

1.66

2.95

1.53

3.64

1.69

TP3

3.27

1.61

3.12

1.66

3.36

1.58

TP4

3.69

1.61

3.31

1.59

3.94

1.57

INT: Behavioral Intention; BE: Brand Equity; ENJ: Enjoyment; TP: Telepresence

Skewness and Kurtosis

Based on Kline (2005), we examined the ratio of the unstandardized skewness and kurtosis indices divided by their standard error. Kline

suggests that values less than 10 indicate no serious skewness or kurtosis problem. ENJ3 is the only item with skewness of -10.3 that exceeds

this threshold. Although ENJ3’s kurtosis value was below 10, it was higher than any other item and so, as an extra check on whether or not

skewness and kurtosis were affecting our results, we reestimated our model after deleting ENJ3 and found that it did not result in any significant

change to the fit indices (i.e., CFI went from 0.978 to 0.982, resulting in a change of only 0.004). The paths and R²s also did not change much.

Therefore, skewness and kurtosis problems were not deemed to be significant problems in our data.

Convergent and Discriminant Validity

Convergent validity was assessed using several methods. First, a general rule states that in the SEM model, the loading of each indicator on

its construct should have a path weight of at least 0.7 (see Hulland 1999). The weights in our measurement model range from 0.75 to 0.96.

Second, to assess reliability, we used Omega coefficients in Mplus (see Raykov and Marcoulides 2010). This model-based reliability statistic

is very similar to Cronbach’s Alpha and is interpreted similarly (e.g., greater than 0.8 as recommended by Cohen 1988), but is computed using

model parameters—specifically the loadings for the indicators on their corresponding latent constructs as well as the indicators’ residual

variances—in order to give a model-specific measure of reliability. Table C2 shows the Omega coefficient for each of the latent constructs.

Third, Fornell and Larcker (1981) recommend that all average variance extracted (AVE) be greater than 0.5; our model’s smallest AVE is 0.65,

which is shown in Table C3 as its square root of 0.80. Hence, the statistics show that there is strong convergent validity in the data.

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Table C2. Omega Coefficients for Constructs

Construct

Omega

Behavioral Intention

0.95

Brand Equity

0.99

Enjoyment

0.99

Telepresence

0.88

Table C3. Correlations of Constructs and Average Variances

Extracted

Construct

INT

BE

ENJ

TP

Behavioral Intention

0.92

Brand Equity

0.49

0.88

Enjoyment

0.54

0.43

0.88

Telepresence

0.50

0.40

0.65

0.80

(*Diagonal represents Square Root of Average Variance Extracted)

Discriminant validity is demonstrated in two ways. First, the AVE rule from Chin et al. (2003) states that the square root of the AVE for each

of the constructs should be greater than that construct’s correlations with the other constructs. As shown in Table C3, the smallest square root

of AVE is 0.80, which is larger than any of the interconstruct correlations. In addition, discriminant validity can be shown through pairwise

factor analysis. To conduct this test, items are taken from each pair of constructs and placed in an EFA in Mplus. The same items are then

placed in a CFA with one factor. If there is a significant chi-square difference, then the items do not load on one factor, which implies that

the two constructs are not actually a single construct. The chi-square difference for each test is significant at the .001 level, which implies that

each pair of two constructs is distinct from one another.

Common Method Variance Test

Common method variance is a phenomenon that occurs when items are artificially correlated with each other (Podsakoff et al. 2003; Podsakoff

and Organ 1986). One method to detect common method variance is to conduct a series of confirmatory factor analyses on the items and force

the number of factors to be one, and then iteratively adding a factor until reaching four factors. If common method variance is present, we

would expect items from different constructs to be more highly correlated with each other and load together. If the result is fewer than the four

factors we expect to obtain, it suggests common method variance. Four CFAs were carried out in Mplus in the following order: one-factor,

two-factor, three-factor, and four-factor. Each model had better fit statistics than the previous model and the chi-square difference test was

significant between each pair of models. This implies that the data loads on four factors, which suggests that the items are not artificially

correlated with each other.

Straub et al. (2004) suggest that reliability coefficients that are

too high

are just as problematic as reliability coefficients that are

too low

when

items are presented in blocks (as in this case). In spite of scale item blocking potentially creating common method variance, it was not sufficient

to fail the common method variance test.

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Appendix D

Mediator Tests

We carried out six mediator tests using Baron and Kenney’s (1986) four-step procedures to assess the mediating effects in Figure 2.

(1) Telepresence as a mediator of the relationship between 2D/3D and Enjoyment

(2) Enjoyment as a mediator of the relationship between Telepresence and Brand Equity

(3) Brand Equity as a mediator of the relationship between Enjoyment and Behavioral Intention

(4) Enjoyment as a mediator of the relationship between Telepresence and Behavioral Intention

(5) Brand Equity as a mediator of the relationship between Telepresence and Behavioral Intention

(6) Telepresence and Enjoyment as mediators of the relationship between 2D/3D and Brand Equity

Telepresence as a Mediator of the Relationship Between 2D/3D and Enjoyment

Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results

Satisfied

The bivariate relationships are significant:

(i) 2D/3D and Telepresence (r = .204, p < .001) and (ii) 2D/3D and Enjoyment (r = .292, p < .001).

Satisfied

Enjoyment = b0 + b1 × (2D/3D) + b2 × Telepresence + e Step 3 is satisfied with b2 = .512 (beta = .565, p < .001) and step 4 is satisfied with b1 = .460 (beta = .177, p < .001). 2D/3D r = .292, p < .001 r = .204, p < .001 Steps 1 and 2 2D/3D Telepresence Enjoyment Enjoyment Telepresence 2D/3D b=.460, ß=.177, p < .001 b=.512, ß=.565, p < .001 Steps 3 and 4

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Enjoyment as a Mediator of the Relationship Between Telepresence and Brand Equity

Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results

Satisfied

2D Condition – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Enjoyment (r = .659, p < .001) and (ii) Telepresence and Brand Equity (r = .373, p < .001).

Satisfied

Brand Equity = b0 + b1 ×

Telepresence + b2 × Enjoyment + e 2D Condition – Satisfied

For step 3, Enjoyment is correlated with Brand Equity, with b2 = .156 (beta = .180, p = .056). Enjoyment is a mediator because it is still (marginally) correlated with Brand Equity even when Telepresence is in the model.

For step 4, the correlation between Telepresence and Brand Equity is such that b1 = .212 (beta = .255, p < .01), which implies that Telepresence and Brand Equity are still correlated even when Enjoyment is in the model.

2D Condition

3D Condition – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Enjoyment (r = .520, p < .001) and (ii) Telepresence and Brand Equity (r = .394, p < .001).

3D Condition – Satisfied

Step 3 is satisfied with b2 = .424 (beta = .375, p < .001) and step 4 is satisfied with b1 = .187 (beta = .199, p < .01), which implies that Tele-presence and Brand Equity are still correlated even when Enjoyment is in the model. 3D Condition Brand Equity Telepresence Telepresence r = .373, p < .001 r = .659, p < .001 Enjoyment Steps 1 and 2 Brand Equity Enjoyment Telepresence b=.212, ß=.255, p < .01 b=.156, ß=.180, p=.056 Steps 3 and 4 Brand Equity Telepresence Telepresence r = .394, p < .001 r = .520, p < .001 Steps 1 and 2 Enjoyment Brand Equity Enjoyment Telepresence b=.187, ß=.199, p < .01 b=.424, ß=.375, p < .001 Steps 3 and 4

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Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 2D and 3D – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Enjoyment (r = .601, p < .001) and (ii) Telepresence and Brand Equity (r = .384, p < .001).

2D and 3D – Satisfied

Step 3 is satisfied with b2 = .279 (beta = .287, p < .001) and step 4 is satisfied with b1 = .187 (beta = .212, p < .001), which implies that Tele-presence and Brand Equity are still correlated even when Enjoyment is in the model.

2D and 3D Combined

Therefore, Enjoyment partially mediates the relationship between Telepresence and Brand Equity, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D com-bined. This result corresponds with the results in Figure 2 in the paper, which also shows that Enjoyment partially mediates the relationship between Telepresence and Brand Equity.

Brand Equity as a Mediator of the Relationship Between Enjoyment and Behavioral Intention

Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results

Satisfied

2D Condition – Satisfied

The bivariate relationships are significant:

(i) Enjoyment and Brand Equity (r = .348, p < .001) and (ii) Enjoyment and Behavioral Intention (r = .452, p < .001).

Satisfied

Behavioral Intention = b0 + b1 × Enjoyment + b2 × (Brand Equity) + e

2D Condition – Satisfied

For step 3, Brand Equity is correlated with Behavioral Intention with b2 = .354 (beta = .257, p < .001). Therefore, Brand Equity is a mediator because it is still correlated with Behavioral Intention even when Enjoyment is in the model. For step 4, the correlation between Enjoyment and Behavioral Intention is such that b1 = .431 (beta = .366, p < .001), which implies that Enjoyment and Behavioral Intention are still correlated even when Brand Equity is in

2D Condition Brand Equity Telepresence Telepresence r = .384, p < .001 r = .601, p < .001 Steps 1 and 2 Enjoyment Brand Equity Enjoyment Telepresence b=.187, ß=.212, p < .001 b=.279, ß=.287, p < .001 Steps 3 and 4 Behavioral Intention Enjoyment Enjoyment r = .452, p < .001 r = .348, p < .001 Steps 1 and 2 Brand Equity Behavioral Intention Enjoyment b=.431, ß=.366, p < .001 b=.354, ß=.257, p < .001 Steps 3 and 4 Brand Equity

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Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 3D Condition – Satisfied

The bivariate relationships are significant:

(i) Enjoyment and Brand Equity (r = .479, p < .001) and (i) Enjoyment and Behavioral Intention (r = .508, p < .001).

3D Condition – Satisfied

Step 3 is satisfied with b2 = .435 (beta = .356, p < .001) and step 4 is satisfied with b1 = .473 (beta = .345, p < .001), which implies that Enjoyment and Behavioral Intention are still correlated even when Brand Equity is in the model.

3D Condition

2D and 3D – Satisfied

The bivariate relationships are significant:

(i) Enjoyment and Brand Equity (r = .414, p < .001) and (ii) Enjoyment and Behavioral Intention (r = .491, p < .001).

2D and 3D – Satisfied

Step 3 is satisfied with b2 = .405 (beta = .314, p < .001) and step 4 is satisfied with b1 = .454 (beta = .371, p < .001), which implies that Enjoyment and Behavioral Intention are still correlated even when Brand Equity is in the model.

2D and 3D Combined

Therefore, Brand Equity partially mediates the relationship between Enjoyment and Behavioral Intention, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D combined. This result corresponds with the results in Figure 2 in the paper, which also shows that Brand Equity partially mediates the relationship between Enjoyment and Behavioral Intention.

Behavioral Intention Enjoyment Enjoyment r = .508, p < .001 r = .479, p < .001 Steps 1 and 2 Brand Equity Behavioral Intention Enjoyment b=.473, ß=.345, p < .001 b=.435, ß=.356, p < .001 Steps 3 and 4 Brand Equity Behavioral Intention Enjoyment Enjoyment r = .491, p < .001 r = .414, p < .001 Steps 1 and 2 Brand Equity Behavioral Intention Enjoyment b=.454, ß=.371, p < .001 b=.405, ß=.314, p < .001 Steps 3 and 4 Brand Equity

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Enjoyment as a Mediator of the Relationship Between Telepresence and Behavioral Intention

Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results

Satisfied

2D Condition – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Enjoy-ment (r = .659, p < .001) and (ii) Telepresence and Behavioral Intention (r = .447, p < .001). Satisfied Behavioral Intention = b0 + b1 × Telepresence + b2 × Enjoyment + e 2D Condition – Satisfied

For step 3, Enjoyment is correlated with Behavioral Intention with b2 = .327 (beta = .278, p < .01). Therefore, Enjoyment is a mediator because it is still correlated with Behavioral Intention even when Telepresence is in the model.

For step 4, the correlation between Telepresence and Behavioral Intention is such that b1 = .297 (beta = .262, p < .01), which implies that Telepresence and Behavioral Intention are still correlated even when Enjoyment is in the model.

2D Condition

3D Condition – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Enjoy-ment (r = .520, p < .001) and (ii) Telepresence and Behavioral Intention (r = .459, p < .001).

3D Condition – Satisfied

Step 3 is satisfied with b2 = .510 (beta = .372, p < .001) and step 4 is satisfied with b1 = .314 (beta = .275, p < .001), which implies that Tele-presence and Behavioral Intention are still correlated even when Enjoyment is in the model. 3D Condition Behavioral Intention Telepresence Telepresence r = .447, p < .001 r = .659, p < .001 Steps 1 and 2 Enjoyment Behavioral Intention Enjoyment Telepresence b=.297, ß=.262, p < .01 b=.327, ß=.278, p < .01 Steps 3 and 4 Behavioral Intention Telepresence Telepresence r = .459, p < .001 r = .520, p < .001 Steps 1 and 2 Enjoyment Behavioral Intention Enjoyment Telepresence b=.314, ß=.275, p < .001 b=.510, ß=.372, p < .001 Steps 3 and 4

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Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 2D and 3D – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Enjoy-ment (r = .601, p < .001) and (ii) Telepresence and Behavioral Intention (r = .466, p < .001).

2D and 3D – Satisfied

Step 3 is satisfied with b2 = .404 (beta = .330, p < .001) and step 4 is satisfied with b1 = .299 (beta = .268, p < .001), which implies that Tele-presence and Behavioral Intention are still correlated even when Enjoyment is in the model.

2D and 3D Combined

Therefore, Enjoyment partially mediates the relationship between Telepresence and Behavioral Intention, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D combined. This result corresponds with the results in Figure 2 in the paper, which also shows that Enjoyment partially mediates the relationship between Telepresence and Behavioral Intention.

Brand Equity as a Mediator of the Relationship Between Telepresence and Behavioral Intention

Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results

Satisfied

2D Condition – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Brand Equity (r = .373, p < .001) and (ii) Telepresence and Behavioral Intention (r = .447, p < .001). Satisfied Behavioral Intention = b0 + b1 × Telepresence + b2 × (Brand Equity) + e 2D Condition – Satisfied

For step 3, Brand Equity is correlated with Behavioral Intention with b2 = .344 (beta = .250, p < .01). Therefore, Brand Equity is a mediator because it is still correlated with Behavioral Intention even when Telepresence is in the model. For step 4, the correlation between Telepresence and Behavioral Intention is such that b = .403 (beta = .356, p < 2D Condition Behavioral Intention Telepresence Telepresence r = .466, p < .001 r = .601, p < .001 Steps 1 and 2 Enjoyment Behavioral Intention Enjoyment Telepresence b=.299, ß=.268, p < .001 b=.404, ß=.330, p < .001 Steps 3 and 4 Behavioral Intention Telepresence r = .447, p < .001 r = .373, p < .001 Brand Equity Steps 1 and 2 Telepresence Behavioral Intention Brand Equity Telepresence b=.403, ß=.356, p < .001 b=.344, ß=.250, p < .01 Steps 3 and 4

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Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results 3D Condition – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Brand Equity (r = .394, p < .001) and (ii) Telepresence and Behavioral Intention (r = .459, p < .001).

3D Condition – Satisfied

Step 3 is satisfied with b2 = .474 (beta = .389, p < .001) and step 4 is satisfied with b1 = .334 (beta = .292, p < .001), which implies that Telepresence and Behavioral Intention are still correlated even when Brand Equity is in the model.

3D Condition

2D and 3D – Satisfied

The bivariate relationships are significant:

(i) Telepresence and Brand Equity (r = .384, p < .001) and (ii) Telepresence and Behavioral Intention (r = .466, p < .001).

2D and 3D – Satisfied

Step 3 is satisfied with b2 = .416 (beta = .322, p < .001) and step 4 is satisfied with b1 = .379 (beta = .339, p < .001), which implies that Telepresence and Behavioral Intention are still correlated even when Brand Equity is in the model.

2D and 3D Combined

Therefore, Brand Equity partially mediates the relationship between Telepresence and Behavioral Intention, and this holds true for both the 2D and 3D conditions separately as well as 2D and 3D

combined. This result corresponds with the results in Figure 2 in the paper, which also shows that Brand Equity partially mediates the relationship between Telepresence and Behavioral Intention.

Behavioral Intention Telepresence r = .459, p < .001 r = .394, p < .001 Brand Equity Steps 1 and 2 Telepresence Behavioral Intention Brand Equity Telepresence b=.334, ß=.292, p < .001 b=.474, ß=.389, p < .001 Steps 3 and 4 Behavioral Intention Telepresence r = .466, p < .001 r = .384, p < .001 Brand Equity Steps 1 and 2 Telepresence Behavioral Intention Brand Equity Telepresence b=.379, ß=.339, p < .001 b=.416, ß=.322, p < .001 Steps 3 and 4

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Telepresence and Enjoyment as Mediators of the Relationship Between 2D/3D and Brand Equity

Results from Steps 1 and 2 Results from Steps 3 and 4 Overall Results

Not Satisfied

Although the bivariate relation-ship between 2D/3D and Telepresence is significant (r = .204, p < .001) and between 2D/3D and Enjoyment is also significant, (r = .292, p < .001), the bivariate relationship between 2D/3D and Brand Equity is not (p = .471).

The reason for the non-significant relationship between 2D/3D and Brand Equity is due to opposing or counteracting forces taking place between 2D/3D and Brand Equity. Since the first and second mediator tests support positive mediating effects of Telepresence and Enjoyment in the model, the non-significant relationship between 2D/3D and Brand Equity is likely caused by a counteracting, negative effect of 2D/3D on Brand Equity which we attribute to distraction-conflict theory.

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Figure

Table C1.  Descriptive Statistics of Indicators
Table C2.  Omega Coefficients for Constructs Construct Omega Behavioral Intention 0.95 Brand Equity 0.99 Enjoyment 0.99 Telepresence 0.88

References

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